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Fig. 1 in Avian trichomonosis mortality events in band-tailed pigeons (Patagioenas fasciata) in California during winter 2014-2015
Fig. 1. Number of band-tailed pigeon (Patagioenas fasciata monolis) mortality reports from phone, email, and online form received by county by the California Department of Fish and Wildlife (CDFW; Rancho Cordova, CA) and the California Department of Public Health (Richmond, CA) between November 2014 and June 2015 in California, U.S.A. (A). Number of band-tailed pigeons admitted to wildlife rehabilitation centers in California, U.S.A. and compiled by county between January and December 2015 (B). Number of band-tailed pigeon carcasses collected by county and received by CDFW between November 2014 and June 2015 in California, U.S.A. (C).
Data for "Sounding out Ecoacoustic Metrics: Avian species richness is predicted by acoustic indices in temperate but not tropical habitats"
<p>This deposit contains the data for the paper <strong>A Multi-habitat, Comparative Evaluation of Ecoacoustic Indices for Biodiversity Monitoring: Acoustic Indices Predict Avian Species Richness in Temperate but not Tropical Habitats. (Ecological Indicators) </strong>The dataset contains a series of 1 min wav files recorded across UK and Ecuadorian habitats. Each one has 26 acoustic indices calculated on it, and a full list of avian species and abundances and GPS data for each sample site.</p> <p>Abstract</p> <p>Affordable, autonomous recording devices facilitate large scale acoustic monitoring and Rapid Acoustic Survey is emerging as a cost-effective approach to ecological monitoring; the success of the approach rests on the development of computational methods by which biodiversity metrics can be automatically derived from remotely collected audio data. Dozens of indices have been proposed to date, but systematic validation against classical, in situ diversity measures. This study conducted the most comprehensive comparative evaluation to date of the relationship between avian species diversity and a suite of acoustic indices across a wide range of ecological conditions. Acoustic surveys were carried out across habitat gradients in temperate and tropical biomes. Baseline avian species richness and subjective multi-taxa biophonic density estimates were established through aural counting by expert ornithologists. 26 acoustic indices were calculated and compared to observed variations in species diversity. Five acoustic diversity indices (Bioacoustic Index, Acoustic Diversity Index, Acoustic Evenness Index, Acoustic Entropy, and the Normalised Difference Sound Index) were assessed as well as three simple acoustic descriptors (root-mean-square, spectral centroid and zero-crossing rate). Highly significant correlations, of up to 65%, between acoustic indices and avian species richness were observed across temperate habitats, supporting the use of automated acoustic indices in biodiversity monitoring where a single vocal taxon dominates. Significant, weaker correlations were observed in neotropical habitats which host multiple non-avian vocalizing species. Multivariate classification analyses suggest that AIs also track observed differences in habitat-dependent community composition and that each habitat has a distinct soundscape. Multivariate analyses of the relative predictive power of AIs show that compound indices are more powerful predictors of avian species richness than any single index and simple descriptors contribute to predicting avian diversity in multi-taxa tropical environments. Our results support the use of community level acoustic indices as a proxy for species richness and point to the potential for tracking of habitat-dependent changes in community composition. Recommendations for the design of compound indices for multi-taxa community composition appraisal are put forward, with consideration for the requirements of next generation, low power remote monitoring networks.</p> <p> </p> <p><strong>Sampling Methods (extract from paper)</strong></p> <p>Acoustic surveys were carried out along a gradient of habitat degradation (1 forested, 2 regenerating forest and 3 agricultural land) in South East (SE) England and North Western (NW) Ecuador. The six sites (UK1, UK2, UK3, EC1, EC2, EC3) were sampled consecutively from May 6th - Aug 25th 2015.</p> <p>All UK sites were in the county of Sussex, in SE England, an area of weald clays (Fig. 2, left) and included ancient woodland (UK1), regenerating farmland with patches of woodland (UK2) and a downland barley farm (UK3).1 min mono audio recordings made every 15 minutes at three different habitats in the UK</p> <p>Ten day acoustic surveys were carried out consecutively at each study site using 15 Wildlife Acoustics Song Meter audio field recorders. Sampling points were arranged in a grid at a minimum distance of 200 m to minimise pseudo replication (the sound of most species being attenuated over this distance in all biomes). Altitudinal range of sample points across sites was minimised in order to prevent introduction of extraneous, confounding gradients (UK varied between 10 m – 50 m and Ecuador 130 m – 390 m). Recording schedules captured 1 min every 15 min around the clock for 10 days at each site, resulting in 960 recordings at each of 15 sample points for 3 habitat types in 2 different climates (86,400 1 minute recordings in total). Data across the 15 sample points was pooled; inter-site variation was not explored in the current analyses. In the UK 3½ hours of each dawn chorus was sampled starting at 1 hour before sunrise. This range was determined to capture the onset, progression and peak of the dawn chorus, creating a temporal gradient. The equatorial dawn chorus is more compact and was sampled for 2¼ hours starting 15 mins before sunrise, capturing a comparable chorus onset and peak.</p> <p> </p> <p> </p>
Data for: Resolving the avian tree of life from top to bottom: The promise and potential boundaries of the phylogenomic era
<p>This data package includes multiple sequence alignments, information about base compositional variation, and phylogenetic analyses that support Figures 7 and 8 in Braun et al. (2019). There is a README in SuppInfo_Braun_et_al_chapter_Kraus_volume.tar.gz that provides a detailed description of the files in this data package.</p> <p>Braun, E.L., Cracraft, J., Houde, P. (2019). Resolving the Avian Tree of Life from Top to Bottom: The Promise and Potential Boundaries of the Phylogenomic Era. In: Kraus, R. (eds) Avian Genomics in Ecology and Evolution. Springer, Cham. https://doi.org/10.1007/978-3-030-16477-5_6</p>
Avian community survey by point count method in a leafy forest of the Station de Biologie des Laurentides, Université de Montréal
<p>Here we provided a dataset of birds’ communities census in the Station de Biologie des Laurentides (SBL), the Université de Montréal field station. Census was performed as part of the BIO2854 field course. BIO2854 is an undergraduate course in terrestrial animal ecology in biological science at Université de Montréal. Highly qualified teaching assistant and students surveyed forest birds over 4 days in May-June 2011 to 2019 by acoustic point-count method. Abundance counts were made based on bird vital domain available in literature and the assumption that each recorded male was associated with one female from the same species. Data are made available to consult for future research and teaching purpose.</p>
Fig. 2 in Sarcocystis falcatula-like derived from opossum in Northeastern Brazil: In vitro propagation in avian cells, molecular characterization and bioassay in birds
Fig. 2. (A) A mature schyzont of Sarcocystis falcatula-like (Sarco-BA1 strain) in a permanent chicken cell line (UMNSAH/DF-1). May-Grüenwald-Giemsa stain. Bar = 20 μm. (B) Extracellular merozoites of Sarco-BA1 on a monolayer of UMNSAH/DF-1 cells. Bar = 10 μm.
Fig. 1 in Sarcocystis falcatula-like derived from opossum in Northeastern Brazil: In vitro propagation in avian cells, molecular characterization and bioassay in birds
Fig. 1. Sporocyst of Sarcocystis falcatula-like. Four sporozoites are visualized inside the sporocyst by light microscopy (A). Autofluorescence of the sporocyst wall is observed after excitation with ultraviolet on a fluorescence microscope (B).
Fig. 1 in Species-specific qPCR assays allow for high-resolution population assessment of four species avian schistosome that cause swimmer's itch in recreational lakes
Fig. 1. Abundance of cercariae by sampling site. Water samples were obtained in mid-June and cercariae abundance was determined using the pan-avian schistosomes qPCR.
Fig. 2 in Species-specific qPCR assays allow for high-resolution population assessment of four species avian schistosome that cause swimmer's itch in recreational lakes
Fig. 2. Percent contribution of T. stagnicolae, T. szidati, T. physellae and A. brantae species to each lake. Water samples from different locations and dates were tested using the species-specific qPCR assay and results were pooled by lake to understand the relative contribution overall of each species to each lake. The percent contribution (based on gene copy number) of each species was calculated.
Fig. 3 in Species-specific qPCR assays allow for high-resolution population assessment of four species avian schistosome that cause swimmer's itch in recreational lakes
Fig. 3. Lifecycles of T. stagnicolae, A. brantae, T. szidati, and T. physellae. life cycle summary of the avian schistosome species targeted for species-specific qPCR tests designed in this study.
Figure 2 in Factors affecting trace element accumulation in livers of avian species from East Poland
Figure 2. RDA results showing the effect of different ecological parameters (the dotted lines) on the concentration of heavy metals in the livers of studied bird species. Monte Carlo permutation test of significance of all canonical axes: P = 0.002. Eigenvalues: axis 1 – 0.197; axis 2 – 0.138. Abbreviations and scales used for analysis: Species: CC - Corvus corax, CF - Corvus frugilegus, CO - Corvus cornix, CM - Corvus monedula, PP - Pica pica, SR - Streptopelia decaocto, AC - Anas platyrhynchos, GA - Garrulus glandarius, SR - Scolopax rusticola, AC - Ardea cinerea, PH - Phalacrocorax carbo, LA - Larus argentatus, LC - Larus canus, CR - Chroicocephalus ridibundus. Food preferences: food F – fish, food I – invertebrates, food O – omnivorous, food P – plants. Foraging area: importance of wetlands for foraging (I wetlands), importance of dumps for foraging (I dumps), importance of urban habitats for foraging (I urban): 0 – none, 1 – small, 2 – medium, 3 – big. Nesting site habitat: nest F – forest, nest U – urban habitat, nest A – aquatic habitat, nest R – rural habitats. Vertebrate carrion importance in food (I carrion): 1 – small, 2 – medium, 3 – big; invertebrate importance in food (I inv): 0 – none, 1 – small, 2 – big. Weight – weight of individuals according to Busse (1990).
Figure 2 in Hematological status of avian species along a metal pollution gradient at Sialkot, Pakistan
Figure 2. Maps showing concentrations of metal (ug/g) in blood samples on different sampling sites of Sialkot.
Figure 1 in Hematological status of avian species along a metal pollution gradient at Sialkot, Pakistan
Figure 1. Maps showing concentrations of metal (ug/g) in feather samples on different sampling sites of Sialkot.
Figure 16 in Mathematical interpretation of avian egg shapes
Figure 16. Ovoid curves: a) Hügelschäffer (according to Obradovic et al., 2013); b) Blaschke (according to Köller, 2000); с) Schauberger (according to Coats, 2001).
Figure 15 in Mathematical interpretation of avian egg shapes
Figure 15. Yamamoto oval (а) (according to Yamamoto, 2020); Cartesian oval (b) (according to Köller, 2000); Möller oval (c) (according to Möller, 2009).
Figure 13 in Mathematical interpretation of avian egg shapes
Figure 13. Cone-like ovoids: a) zeta-curve (Stadnicki, 2015); b) Phalacrocorax carbo; c) Anser anser; d) Phalacrocorax pelagicus; e) Grus canadensis; f) Grus grus.
Figure 14 in Mathematical interpretation of avian egg shapes
Figure 14. Cartesian ovals: а) with p = 1, q = 2 and c = 5; b) with p = 1, q = 3 and c = 5 (Beverlin, 2006); с) hyperbolic cone (Kirsh, 2010).
Fig. 10 in Mathematical interpretation of avian egg shapes
Fig. 10. Spher-obtuse аsymmetric pseudo-ovoid: 1, 9, 17) construction of ovoids in the shape matrix; 2, 10, 18) egg profile diagram; bird egg profiles: 3) Accipiter gentilis; 4) Accipiter nisus; 5) Falco vespertinus; 6) Falco tinnunculus; 7) Panurus biarmicus; 8) Asio otus; 11) Milvus milvus; 12) Hieraaetus pennatus; 13) Picus canus; 14) Falco cherrug; 15) Columba oenas; 16) Falco tinnunculus; 19) Dendrocopos major; 20) Cuculus canorus; 21) Sylvia atricapilla; 22) Fringilla coelebs; 23) Crex crex; 24) Phasianus colchicus.
Fig. 9 in Mathematical interpretation of avian egg shapes
Fig. 9. Аsymmetric sphere-rounded pseudo-ovoid: 1, 9, 17) construction of ovoids in the shape matrix; 2, 10, 18) egg profile diagram; bird egg profiles: 3) Egretta alba; 4) Podiceps nigricollis; 5) Porzana parva; 6) Ardea cinerea; 7) Cygnus olor; 8) Podiceps cristatus; 11) Cygnus olor; 12) Casuarius casuarius; 13) Egretta garzetta; 14) Mergus serrator; 15) Egretta alba; 16) Phalacrocorax carbo; 19) Anser anser; 20) Pelecanus crispus; 21) Podiceps grisegena; 22) Cygnus olor; 23) Phalacrocorax carbo; 24) Podiceps cristatus,
Figure 12 in Mathematical interpretation of avian egg shapes
Figure 12. Rounded -drop-shaped asymmetric pseudo-ovoid: 1, 9, 17) construction of ovoids in the matrix of forms; 2, 10, 18) egg profile diagram; bird egg profiles: 3) Oriolus oriolus; 4) Podiceps nigricollis; 5) Remiz pendulinus; 6) Porzana porzana; 7) Procellaria leucomelas; 8) Fulica atra;11) Catharacta maccormicki; 12) Corvus corax; 13) Phalacrocorax pelagicus; 14) Riparia riparia; 15) Larus genei; 16) Alauda arvensis; 19) Platalea leucorodia; 20) Upupa epops; 21) Larus cachinnans; 22) Pica pica; 23) Lunda cirrhata; 24) Gavia arctica.
Fig. 11 in Mathematical interpretation of avian egg shapes
Fig. 11. Asymmetric rounded-typical pseudo-ovoids: 1, 9, 17) construction of ovoids in the shape matrix; 2), 10), 18) egg profile diagram; bird egg profiles: 3) Fringilla coelebs; 4) Tetrastes bonasia; 5) Chloris chloris; 6) Fulica atra; 7) Sturnus vulgaris; 8) Turdus merula; 11) Acrocephalus palustris; 12) Turdus viscivorus; 13) Cuculus canorus; 14) Oriolus oriolus; 15) Luscinia luscinia; 16) Dendrocopos major; 19) Haematopus ostralegus; 20) Remiz pendulinus; 21) Porzana porzana; 22) Anthropoides virgo; 23) Cygnus cygnus;24) Anas strepera.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.